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In autonomous driving system, lane change decision-making plays an important role as the front-end of lateral control. However, the current prediction methods of lane change are typically performed by using basic variables as the features of model without deep processing, which reduces the accuracy of the prediction. Therefore, we propose a binary logistic regression method to solve the lane change decision problem under expressway conditions, which treat quantified willingness and risk as the inputs. Firstly, we design Lane Changing Willingness function and Lane Changing Risk function with the minimum safety spacing theory and traffic environment factors. Secondly, a binary logistic regression method for predicting lane change behavior is proposed. Thirdly, we develop the driving simulation platform with low latency data collecting tools and design the experiments.

At present, the research on fatigue driving at home and abroad mainly has the following three methods: (i) driving behavioral (vehicle-based), (ii) driver behavioral (video-based), and (iii) driver physiological signals measure. The physiology-based methods have the highest recognition result. When drivers are in a state of fatigue, the Autonomic Nervous System (ANS) activity will be reflected from the physiological signal. Most of the contact sensors are used to obtain the physiological signal information of the driver. However, the contact sensors will affect the driver's driving operation, so this paper uses the frequency-modulated continuous-wave (FMCW) radar to collect the physiological signals. A fatigue driving simulation experiment was designed to collect experimental subjects' physiological signal data and separate the steady heartbeat and respiratory signals.

Target detection is essential to the advanced driving assistance system (ADAS) and automatic driving. And the data fusion of millimeter wave radar and camera could provide more accurate and complete information of targets and enhance the environmental perception performance. In this paper, a method of vehicle and pedestrian detection based on the data fusion of millimeter wave radar and camera is proposed to improve the target distance estimation accuracy. The first step is the targets data acquisition. A deep learning model called Single Shot MultiBox Detector (SSD) is utilized for targets detection in consecutive video frames captured by camera and further optimized for high real-time performance and accuracy. Secondly, the coordinate system of camera and radar are unified by coordinate transformation matrix. Then, the parallel Kalman filter is used to track the targets detected by radar and camera respectively.

Real-time and reliable perception of the surrounding environment is an important prerequisite for advanced driving assistance system (ADAS) and automatic driving. And vision-based detection plays a significant role in environment perception for automatic vehicles. Although deep convolutional neural networks enable efficient recognition of various objects, it has difficulty in accurately detecting special vehicles, rocks, road pile, construction site, fence and so on. In this work, we address the task of traffic scene understanding with semantic image segmentation. Both driveable area and the classification of object can be attained from the segmentation result. First, we define 29 classes of objects in traffic scenarios with different labels and modify the Deeplab V2 network. Then in order to reduce the running time, MobileNet architecture is applied to generate the feature map instead of the original models.

Estimating the motion state of peripheral targets is a very important part in the environment perception of intelligent vehicles. The accurate estimation of the motion state of the peripheral targets can provide more information for the intelligent vehicle planning module which means the intelligent vehicle is able to anticipate hazards ahead of time. To get the motion state of the target accurately, the target’s range, velocity, orientation angle and yaw rate need to be estimated. Three-dimensional high-resolution automotive millimeter wave radar can measure radial range, radial velocity, azimuth angle and elevation angle about multiple reflections of an extended target. Thus, the three-dimensional range information and three-dimensional velocity information can be obtained. With multiple three-dimensional high-resolution automotive millimeter-wave radar, it is possible to measure information in various directions of a target.

In the autonomous driving perception task, the accuracy of target detection is an essential evaluation, especially for small targets. In this work, we propose a multi-sensor fusion neural network that combines radar and image data to improve the confidence level of the camera when detecting targets and the accuracy of the prediction box regression. The fusion network is based on the basic structure of single-shot multi-box detection (SSD). Inspired by the attention mechanism in image processing, our work incorporates the a priori knowledge of radar detection in the convolutional block attention module (CBAM), which forms a new attention mechanism module called radar convolutional block attention module (RCBAM). We add the RCBAM into the SSD target detection network to build a deep neural network fusing millimeter-wave radar and camera.

With the increasing intelligence of human society, people's demand for human-computer interaction is also increasing. As an important communication medium for human to express information, gesture has always been an important topic in human-computer interaction. Using gesture recognition technology in the vehicle environment can reduce the operation difficulty during driving, reduce the possibility of driver distraction, and greatly improve driving safety and driving experience. Millimeter wave radar can effectively protect the privacy in the car from being leaked, and can still work normally in the dark interior environment. Moreover, with the development of millimeter wave technology from 24g to 60g and 77g, the improvement of its resolution further improves its ability to detect small displacement. Therefore, the gesture recognition technology using millimeter wave radar has been developed.